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Artificial Intelligence (AI) technology is creating a huge buzz and is considered to be the future technology. It has revolutionized all industries by utilizing intelligent algorithms to train the models and perform with logic and accuracy. AI has made its presence in software testing too and has improved the quality assurance process by multiple folds. It has increased productivity, consistency, accuracy, and agility while ensuring reduced time, cost, and effort considerably. Though the issues in software quality improvement were resolved significantly by codeless test automation tools, speed was still a concern.

The current Quality Assurance market is not static, it's changing rapidly. In order to adapt and adjust to these transformations and stay competitive, businesses need to be aware of and follow the latest industry trends. They can help your company meet your business demands and build connected, scalable, intelligent and fast digital solutions for your clients. Those who are able to predict the future of QA will become industry leaders. Thus, we have decided to take a look at the main QA trends that will shape the future over the next few years.

This was the year that saw GitHub Copilot move from a plug-in on Jetbrain, where it was first launched in 2021, to broad availability for the Visual Studio IDE in March. It was followed by the release of Amazon's code completion service, Code Whisperer, in June, and Replit's Ghostwriter in October. Tabnine, an AI startup for code generation, secured $15.5 million in funding, while another code-completion startup, Kite, died in the wake of Copilot's popularity. And then, too, by the end of the year, it all ended up as a big question mark when GitHub wound up in litigation over its use of open source repositories in Copilot. Although much of the focus in 2022 was on automated coding and code completion, it turns out that AI technologies transformed code in more subtle ways in the past year. "We don't believe we're going to see AI replace DevOps engineers or platform engineers, but really augment them," said Zach Zaro, co-founder and CEO of Coherence, a DevOps automation startup that leverages AI. "You have a lot happening at the application layer level -- AI coming to help developers write application code, not infrastructure code."

Manual testing can take hours and make continuous development difficult unless you have access to unlimited resources. Accuracy is also an issue – testers are only human and can easily miss small changes. Software testing is subject to error in organizations that rely solely on manual testing and often presents a bottleneck. Many businesses are now combining automation with manual testing in order to speed up the process. Teams can carry out test cycles faster by automating repeated test cases, leaving the manual limited to defining the case, reviewing outputs, and carrying out a final quality assurance (QA) overview.

It is an important process that ensures customer satisfaction in the application. It is the planned way in test automation where an application observed under specific conditions where the testers understand the threshold and the risks involved in the software implementation. AI in Software Testing helps to safeguard and an application against potential application fail-overs which may turn out being harmful to the application and the organization later on. As more and more Artificial Intelligence comes into our lives, the need for testing with it is increasing. Taking the self-driving cars as an example: if the car's intelligence does not work properly and it makes a wrong decision, or the response time is slow, it could easily result in a car crash and puts human life in danger.

Every business is caught between a demand to deliver things quickly and make sure they meet quality expectations. It doesn't matter whether they make furniture, cars, food or software, companies have to meet the demands of customers that want fast access to great products. This puts a squeeze on development. First mover advantage counts for a lot, and many companies look for ways to accelerate their go-to-market. That can mean certain areas, such as quality assurance and testing, are cut back to the bare minimum: what's required by law, for example, or what's achievable by stretched teams within a curtailed timeframe.

Don't get tripped up by thinking of the wrong kind of artificial intelligence (AI) when it comes to testing scenarios. In fact, this second type of AI is already being used in some testing scenarios. But before looking at automation-testing examples affected by machine learning, you need to define what machine learning (ML) actually is. At its core, ML is a pattern-recognition technology--it uses patterns identified by your machine learning algorithms to predict future trends. ML can consume tons of complex information and find patterns that are predictive, and then alert you to those differences.

It's important to acknowledge that RPA is not new. It is an extension of the record-and-playback approach used in test automation. But where RPA differs from test automation is that while test automation is meant to check for any break in application functionality by automating regression tests, RPA is used in automating business processes. While people commonly say that test automation involves code and RPA doesn't, there are scenarios where RPA also involves some coding. Now, test automation has evolved over time, and there is a clear approach on how to get started with test automation and use it to the best extent possible to achieve the results that an organization wants from it.

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